A Local Least Squares Framework for Ensemble Filtering

By Jeffrey L. Anderson. 2003

471 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/632ab0b08a26979724a7e4bdbb837a2f98f58ea2

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Citations in the corpus

An adaptive covariance inflation error correction algorithm for ensemble filters (2007, methodology): “Methods can also be derived directly from (2.7, 2.10) as in Anderson (2003) resulting in identical algorithms in many cases.”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, methodology): “All experiments use the ensemble adjustment Kalman filter (EAKF; Anderson 2003).”
Model Error Estimation Employing an Ensemble Data Assimilation Approach (2006, background): “More discussion about the differences in the current approaches can be found in Evensen (2003), Anderson (2003), and Tippett et al. (2003).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “We also perturbed boundary conditions using the Data Assimilation Research Test bed (DART) system (Anderson 2001, 2003).”
Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation (2012, background): “Describing the impact of a single observation y on a single state variable component x is sufficient to define all commonly used ensemble filter algorithms without loss of generality (see Anderson 2003 for a derivation).”
Filtering Complex Turbulent Systems (2012, methodology): “In the second direction, Bayesian hierarchical modeling [17] and reduced order filtering strategies [109, 56, 128, 8, 9, 25, 42, 43, 114, 70, 63] based on the Kalman filter [7, 27, 74] have been developed with some success in these extremely complex high dimensional nonlinear systems.”
System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies (2007, methodology): “The other important advantage of ensemble-based filters is that error covariances used in regression are flow dependent and temporally varying (Zhang and Anderson 2003).”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, methodology): “A similar approach to simulating 3DVar in an ensemble filter framework was described in Zhang and Anderson (2003) and Evensen (2003).”
An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation (2013, methodology): “The filtering process is implemented by a multivariate linear regression with careful consideration of temperature and salinity covariance (Anderson 2003).”
The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var (2003, methodology): “Such methods have been demonstrated successfully for simple low order models (Pham 2001; Anderson 2003).”
Asynchronous data assimilation with the EnKF (2010, methodology): “The second method uses a local approximation of the forecast covariance for updating a state vector element, calculated by building a local window around this element (Anderson, 2003; Evensen, 2003; Ott et al., 2004). Following Evensen (2003), we will refer to it as local analysis.”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “All these methods require considerable A u t h o r ' s p e r s o n a l”
An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation (2007, methodology): “For applications to nonlinear assimilation problems, it is useful to think of the ensemble Kalman filter as a least squares method that obtains an averaged gradient for minimization not from a variational approach but from an empirical correlation between model variables (Anderson 2003; Zafari et al”
Relation between two common localisation methods for the EnKF (2011, background): “The initial formulations of LA [1, 9, 17] did not modify observations and ensemble members within local windows.”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, methodology): “A summary of the square root filters by Bishop et al. (2001), Anderson (2001), and Whitaker and Hamill (2002) has been given by Tippet et al. (2003), and see also the general discussion of ensemble methods in a ‘‘local least-squares framework’’ given by Anderson (2003) .”
An economical approach to four-dimensional variational data assimilation (2010, background): “Anderson (2003) mentioned that there are practical limitations on the number of observations to be included.”
Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, unknown): “…background on the application of the EnKF in atmospheric sciences, see, for example, Mitchell and Houtekamer (2000), Keppenne (2000), Hamill and Snyder (2000), Anderson (2001, 2003), Keppenne and Rienecker (2002), Snyder and Zhang (2003), Zhang et al. (2004, 2006), and Houtekamer et al. (2005).”